Flow cytometry is a powerful method, which is widely used for high‐throughput quantitative and qualitative analysis of cells. However, its straightforward applicability for extracellular vesicles (EVs) and mainly exosomes is hampered by several challenges, reflecting mostly the small size of these vesicles (exosomes: ~80–200 nm, microvesicles: ~200–1,000 nm), their polydispersity, and low refractive index. The current best and most widely used protocol for beads‐free flow cytometry of exosomes uses ultracentrifugation (UC) coupled with floatation in sucrose gradient for their isolation, labeling with lipophilic dye PKH67 and antibodies, and an optimized version of commercial high‐end cytometer for analysis. However, this approach requires an experienced flow cytometer operator capable of manual hardware adjustments and calibration of the cytometer. Here, we provide a novel and fast approach for quantification and characterization of both exosomes and microvesicles isolated from cell culture media as well as from more complex human samples (ascites of ovarian cancer patients) suitable for multiuser labs by using a flow cytometer especially designed for small particles, which can be used without adjustments prior to data acquisition. EVs can be fluorescently labeled with protein‐(Carboxyfluoresceinsuccinimidyl ester, CFSE) and/or lipid‐ (FM) specific dyes, without the necessity of removing the unbound fluorescent dye by UC, which further facilitates and speeds up the characterization of microvesicles and exosomes using flow cytometry. In addition, double labeling with protein‐ and lipid‐specific dyes enables separation of EVs from common contaminants of EV preparations, such as protein aggregates or micelles formed by unbound lipophilic styryl dyes, thus not leading to overestimation of EV numbers. Moreover, our protocol is compatible with antibody labeling using fluorescently conjugated primary antibodies. The presented methodology opens the possibility for routine quantification and characterization of EVs from various sources. Finally, it has the potential to bring a desired level of control into routine experiments and non‐specialized labs, thanks to its simple bead‐based standardization.

The Cdk12/CycK complex promotes expression of a subset of RNA polymerase II genes, including those of the DNA damage response. CDK12 is among only nine genes with recurrent somatic mutations in high-grade serous ovarian carcinoma. However, the influence of these mutations on the Cdk12/CycK complex and their link to cancerogenesis remain ill-defined. Here, we show that most mutations prevent formation of the Cdk12/CycK complex, rendering the kinase inactive. By examining the mutations within the Cdk12/CycK structure, we find that they likely provoke structural rearrangements detrimental to Cdk12 activation. Our mRNA expression analysis of the patient samples containing the CDK12 mutations reveals coordinated downregulation of genes critical to the homologous recombination DNA repair pathway. Moreover, we establish that the Cdk12/CycK complex occupies these genes and promotes phosphorylation of RNA polymerase II at Ser2. Accordingly, we demonstrate that the mutant Cdk12 proteins fail to stimulate the faithful DNA double strand break repair via homologous recombination. Together, we provide the molecular basis of how mutated CDK12 ceases to function in ovarian carcinoma. We propose that CDK12 is a tumor suppressor of which the loss-of-function mutations may elicit defects in multiple DNA repair pathways, leading to genomic instability underlying the genesis of the cancer.

Extracellular vesicles (EVs) function as important conveyers of information between cells and thus can be exploited as drug delivery systems or disease biomarkers. Transmission electron microscopy (TEM) remains the gold standard method for visualisation of EVs, however the analysis of individual EVs in TEM images is time‐consuming if performed manually. Therefore, we present here a software tool for computer‐assisted evaluation of EVs in TEM images. TEM ExosomeAnalyzer detects EVs based on their shape and edge contrast criteria and subsequently analyses their size and roundness. The software tool is compatible with common negative staining protocols and isolation methods used in the field of EV research; even with challenging TEM images (EVs both lighter and darker than the background, images containing artefacts or precipitated stain, etc.). If the fully‐automatic analysis fails to produce correct results, users can promptly adjust the detected seeds of EVs as well as their boundaries manually. The performance of our tool was evaluated for three different modes with variable levels of human interaction, using two datasets with various heterogeneity. The semi‐automatic mode analyses EVs with high success rate in the homogenous dataset (F1 score 0.9094, Jaccard coefficient 0.8218) as well as in the highly heterogeneous dataset containing EVs isolated from cell culture medium and patient samples (F1 score 0.7619, Jaccard coefficient 0.7553). Moreover, the extracted size distribution profiles of EVs isolated from malignant ascites of ovarian cancer patients overlap with those derived by cryo‐EM and are comparable to NTA‐ and TRPS‐derived data. In summary, TEM ExosomeAnalyzer is an easy‐to‐use software tool for evaluation of many types of vesicular microparticles and is available at http://cbia.fi.muni.cz/exosome‐analyzer free of charge for non‐commercial and research purposes. The web page contains also detailed description how to use the software tool including a video tutorial.

Small extracellular vesicles (sEVs) are cell-derived vesicles of nanoscale size (~30–200 nm) that function as conveyors of information between cells, reflecting the cell of their origin and its physiological condition in their content. Valuable information on the shape and even on the composition of individual sEVs can be recorded using transmission electron microscopy (TEM). Unfortunately, sample preparation for TEM image acquisition is a complex procedure, which often leads to noisy images and renders automatic quantification of sEVs an extremely difficult task. We present a completely deep-learning-based pipeline for the segmentation of sEVs in TEM images. Our method applies a residual convolutional neural network to obtain fine masks and use the Radon transform for splitting clustered sEVs. Using three manually annotated datasets that cover a natural variability typical for sEV studies, we show that the proposed method outperforms two different state-of-the-art approaches in terms of detection and segmentation performance. Furthermore, the diameter and roundness of the segmented vesicles are estimated with an error of less than 10%, which supports the high potential of our method in biological applications.